#### Coffee_EDA_Questions
1) Read the dataset<br>
2) Remove/handle the null values<br>
3) Depict different Market types and their count on a bar chart<br>
4) Depict different Product types and their count on a line chart<br>
5) Depict different State and their count on a pie chart<br>
6) Depict Market wise sum of Profit and depict the result on bar chart.<br>
7) Depict Product wise mean of Sales and depict the result on bar chart and line chart superimposed<br>
8) Depcit Profit vs Sales on a Scatter Chart where Product is 'Regular Espresso'<br>
9) Depcit Budget Profit vs Budget Sales on a Scatter Chart where State is California and Product is 'Earl Grey'<br>
10) Depcit Market wise mean of Budget Sales and mean of Sales in a stacked bar chart<br>
11) Plot the Frequency distribution of Total Expenses<br>
12) Compare Frequency Distribution of Profit and Budget Profit on a histogram for the Oregon State<br>
13) Compare Frequency of COGS and Budget COGS on a histogram for the Eastern Market for the Product Cafe Mocha<br>
14) Compare Sales vs Profit on a histogram for the Eastern or Central Market and for Decaf Type<br>
15) Depict the quartile distribution of COGS where Sales is greater than 400<br>
16) Depict the quartile distribution of Budget Profit for Decaf Espresso<br>
17) Find the Market, Product and Type where Sales is maximum<br>
18) Find State and Market with Maximum Profit or Maximum Budget Profit<br>
19) Find Product, State and Market where Total Exepnses are minimum<br>
20) Statisically infer the State, Product and Market chosen to further expnad/invest into the Coffee Business based on Sales, Profit and Total Expenses<br>
21) Statisically infer the State, Product and Market chosen to further withhold the Coffee Business based on Sales, Profit and Total Expenses.
22) Depict Market and Type wise sum of Budget Profit on a bar chart<br>
23) Depict Product Type and State wise mean of Sales on a area chart<br>
24) Depict Product and Type wise sum of Budget COGS on a line Chart<br>
25) Depict Product and Type wise sum of Budget Profit and Budget Sales on a line Chart<br>
26) Depict Market and Product Type based mean of Sales on abar chart and on a stacked bar chart separately<br>
27) Find State and Market Size based mean of Sales and COGS where Market is not Central and Profit is over 350. Depict the result on a line chart, stacked bar and area chart separately<br>
28) Find Product and Market Size based mean of Budget COGS and Inventory where Type is Regular and neither product or its Type is Espresso. Depict the result on a line chart, stacked bar and area chart separately <br>
<b>Seaborn EDA</b><br>
1) Depict Product based on Market Size on a count plot<br>
2) Depict State based on Market on a count plot<br>
3) Depict the Product Type vs Total Expenses on a Violin Plot based on Market<br>
4) Depict Market vs Budget COGS on a boxplot<br>
5) Depict Product vs Margin on a boxplot based on Market Size<br>
6) Depict the Market vs Budget Sales on a Violin Plot based on Product Type where product is Colombina or Darjeeling<br>
7) Depict the Product vs Inventory on a Box Plot based on Product Type where State is California or Nevada<br>
8) Generate Pairplot for the Coffee dataset where Type is Regular including fields- Budget Sales, Budget Profit, Budget COGS, Sales, Proift and COGS<br>
9) Generate Pairplot for the Coffee dataset where Profit is over 250 based on Type including fields- Budget Sales, Budget Profit, Budget COGS, Sales, Proift and COGS<br>
10) Generate Pairplot for the Coffee dataset where Market is Western or Central based and Product Line is beans based on Type including fields- COGS, Sales, Proift and Total Expenses<br>
11) Depict Product vs Sales based on Market on a Strip Plot where Type is Decaf and Market is not Central<br>
12) Depict State vs Budget Profit based on Product Type on a Strip Plot where Market is not Western and Sales >300<br>
13) Depict Market vs Total Expenses based on Type on a Swarm Plot where Sales is over 200 and Market Size is Small<br>
14) Depict correlation values on a heatmap<br>
15) Depict Budget Sales vs Sales on a Joint Plot<br>
16) Depict Total Expenses on Joint plot where Sales>300 or Profit>300 where joint plot kind is scatter<br>
17) Depict Budget COGS on Joint plot where Market is Eastern and Type is Regular where joint plot kind is hex
<br>
18) Depict Marketing on Joint plot where Market is not Central and Sales is over 500 where joint plot kind is kde<br>
df.isnull().sum()
Area Code 0 Date 0 Market 0 Market Size 0 Product 0 Product Line 0 Product Type 0 State 0 Type 0 Budget COGS 0 Budget Margin 0 Budget Profit 0 Budget Sales 0 COGS 0 Inventory 0 Margin 0 Marketing 0 Profit 0 Sales 0 Total Expenses 0 dtype: int64
d2 = df['Product Type'].value_counts()
d2
Espresso 1176 Coffee 1056 Herbal Tea 1056 Tea 960 Name: Product Type, dtype: int64
from collections import Counter
d4 = df['State'].value_counts()
d4
#m = Counter(df['State'])
#print(m)
#plt.pie(m,labels='State')
#plt.show
California 288 Utah 288 Nevada 264 Oregon 264 Colorado 264 Washington 240 Illinois 216 Ohio 216 Missouri 216 Wisconsin 216 Florida 216 Iowa 216 New York 192 New Hampshire 168 Texas 168 Louisiana 168 Connecticut 168 Oklahoma 168 New Mexico 168 Massachusetts 144 Name: State, dtype: int64
plt.figure(figsize=(6,6))
plt.plot(d4.index,d4,marker='o')
plt.xticks(d4.index,d4,rotation=90)
plt.pie(d4,labels=d4.index,autopct='%0.2f%%')
#plt.pie(d4.index,labels='State')
plt.show()
import matplotlib.pyplot as plt
plt.bar(d2.index,d2,color='red')
plt.plot(d2.index,d2,color='green',marker='o')
plt.xlabel('product type')
plt.ylabel('product count')
plt.show()
# in case of multiindex series or dataframe we will use pandas chart
#
when two column or more came it is data frame
#df.groupby(['Market'],['Type'])['Budget Profit'].sum()
plt.bar
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv("Coffee.csv")
df
o = df.groupby(['Market','Type'])['Budget Profit'].sum()
print(o)
#plt.pie(o,labels=j,shadow=False,autopct='%0.2f%%')
#plt.show()
Market Type
Central Decaf 43880
Regular 48700
East Decaf 10800
Regular 45980
South Decaf 16320
Regular 18720
West Decaf 36240
Regular 38120
Name: Budget Profit, dtype: int64
from collections import Counter
d4 = df['State'].value_counts()
d4
Utah 288 California 288 Oregon 264 Colorado 264 Nevada 264 Washington 240 Missouri 216 Illinois 216 Wisconsin 216 Iowa 216 Florida 216 Ohio 216 New York 192 Connecticut 168 Oklahoma 168 Texas 168 New Mexico 168 New Hampshire 168 Louisiana 168 Massachusetts 144 Name: State, dtype: int64
plt.figure(figsize=(6,6))
plt.plot(d4.index,d4,marker='o')
plt.xticks(d4.index,d4,rotation=90)
plt.pie(d4,labels=d4.index,autopct='%0.2f%%')
#plt.pie(d4.index,labels='State')
plt.show()
df
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 719 | 40909 | Central | Major Market | Amaretto | Beans | Coffee | Colorado | Regular | 90 | 130 | 100 | 220 | 89 | 777 | 130 | 24 | 94 | 219 | 36 |
| 1 | 970 | 40909 | Central | Major Market | Colombian | Beans | Coffee | Colorado | Regular | 80 | 110 | 80 | 190 | 83 | 623 | 107 | 27 | 68 | 190 | 39 |
| 2 | 970 | 40909 | Central | Major Market | Decaf Irish Cream | Beans | Coffee | Colorado | Decaf | 100 | 140 | 110 | 240 | 95 | 821 | 139 | 26 | 101 | 234 | 38 |
| 3 | 303 | 40909 | Central | Major Market | Green Tea | Leaves | Tea | Colorado | Regular | 30 | 50 | 30 | 80 | 44 | 623 | 56 | 14 | 30 | 100 | 26 |
| 4 | 303 | 40909 | Central | Major Market | Caffe Mocha | Beans | Espresso | Colorado | Regular | 60 | 90 | 70 | 150 | 54 | 456 | 80 | 15 | 54 | 134 | 26 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4243 | 206 | 41609 | West | Small Market | Caffe Latte | Beans | Espresso | Washington | Regular | 20 | 30 | 20 | 50 | 24 | 567 | 32 | 7 | 19 | 60 | 19 |
| 4244 | 509 | 41609 | West | Small Market | Caffe Mocha | Beans | Espresso | Washington | Regular | 60 | 80 | 30 | 140 | 65 | 403 | 80 | 24 | 34 | 155 | 57 |
| 4245 | 360 | 41609 | West | Small Market | Decaf Espresso | Beans | Espresso | Washington | Decaf | 70 | 100 | 60 | 170 | 80 | 1079 | 96 | 24 | 76 | 188 | 45 |
| 4246 | 360 | 41609 | West | Small Market | Colombian | Beans | Coffee | Washington | Regular | 80 | 120 | 80 | 200 | 72 | 461 | 104 | 23 | 86 | 188 | 46 |
| 4247 | 206 | 41609 | West | Small Market | Decaf Irish Cream | Beans | Coffee | Washington | Decaf | 120 | 170 | 50 | 290 | 105 | 716 | 145 | 95 | 30 | 266 | 125 |
4248 rows × 20 columns
df.shape
(4248, 20)
j = df['Market'].unique().tolist()
print(j)
#print([j.index])
['Central', 'East', 'South', 'West']
u = df['Type'].unique().tolist()
print(u)
['Regular', 'Decaf']
df.info
<bound method DataFrame.info of Area Code Date Market Market Size Product Product Line \
0 719 40909 Central Major Market Amaretto Beans
1 970 40909 Central Major Market Colombian Beans
2 970 40909 Central Major Market Decaf Irish Cream Beans
3 303 40909 Central Major Market Green Tea Leaves
4 303 40909 Central Major Market Caffe Mocha Beans
... ... ... ... ... ... ...
4243 206 41609 West Small Market Caffe Latte Beans
4244 509 41609 West Small Market Caffe Mocha Beans
4245 360 41609 West Small Market Decaf Espresso Beans
4246 360 41609 West Small Market Colombian Beans
4247 206 41609 West Small Market Decaf Irish Cream Beans
Product Type State Type Budget COGS Budget Margin \
0 Coffee Colorado Regular 90 130
1 Coffee Colorado Regular 80 110
2 Coffee Colorado Decaf 100 140
3 Tea Colorado Regular 30 50
4 Espresso Colorado Regular 60 90
... ... ... ... ... ...
4243 Espresso Washington Regular 20 30
4244 Espresso Washington Regular 60 80
4245 Espresso Washington Decaf 70 100
4246 Coffee Washington Regular 80 120
4247 Coffee Washington Decaf 120 170
Budget Profit Budget Sales COGS Inventory Margin Marketing Profit \
0 100 220 89 777 130 24 94
1 80 190 83 623 107 27 68
2 110 240 95 821 139 26 101
3 30 80 44 623 56 14 30
4 70 150 54 456 80 15 54
... ... ... ... ... ... ... ...
4243 20 50 24 567 32 7 19
4244 30 140 65 403 80 24 34
4245 60 170 80 1079 96 24 76
4246 80 200 72 461 104 23 86
4247 50 290 105 716 145 95 30
Sales Total Expenses
0 219 36
1 190 39
2 234 38
3 100 26
4 134 26
... ... ...
4243 60 19
4244 155 57
4245 188 45
4246 188 46
4247 266 125
[4248 rows x 20 columns]>
df.describe
<bound method NDFrame.describe of Area Code Date Market Market Size Product Product Line \
0 719 40909 Central Major Market Amaretto Beans
1 970 40909 Central Major Market Colombian Beans
2 970 40909 Central Major Market Decaf Irish Cream Beans
3 303 40909 Central Major Market Green Tea Leaves
4 303 40909 Central Major Market Caffe Mocha Beans
... ... ... ... ... ... ...
4243 206 41609 West Small Market Caffe Latte Beans
4244 509 41609 West Small Market Caffe Mocha Beans
4245 360 41609 West Small Market Decaf Espresso Beans
4246 360 41609 West Small Market Colombian Beans
4247 206 41609 West Small Market Decaf Irish Cream Beans
Product Type State Type Budget COGS Budget Margin \
0 Coffee Colorado Regular 90 130
1 Coffee Colorado Regular 80 110
2 Coffee Colorado Decaf 100 140
3 Tea Colorado Regular 30 50
4 Espresso Colorado Regular 60 90
... ... ... ... ... ...
4243 Espresso Washington Regular 20 30
4244 Espresso Washington Regular 60 80
4245 Espresso Washington Decaf 70 100
4246 Coffee Washington Regular 80 120
4247 Coffee Washington Decaf 120 170
Budget Profit Budget Sales COGS Inventory Margin Marketing Profit \
0 100 220 89 777 130 24 94
1 80 190 83 623 107 27 68
2 110 240 95 821 139 26 101
3 30 80 44 623 56 14 30
4 70 150 54 456 80 15 54
... ... ... ... ... ... ... ...
4243 20 50 24 567 32 7 19
4244 30 140 65 403 80 24 34
4245 60 170 80 1079 96 24 76
4246 80 200 72 461 104 23 86
4247 50 290 105 716 145 95 30
Sales Total Expenses
0 219 36
1 190 39
2 234 38
3 100 26
4 134 26
... ... ...
4243 60 19
4244 155 57
4245 188 45
4246 188 46
4247 266 125
[4248 rows x 20 columns]>
df.fillna(method='bfill')
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 719 | 40909 | Central | Major Market | Amaretto | Beans | Coffee | Colorado | Regular | 90 | 130 | 100 | 220 | 89 | 777 | 130 | 24 | 94 | 219 | 36 |
| 1 | 970 | 40909 | Central | Major Market | Colombian | Beans | Coffee | Colorado | Regular | 80 | 110 | 80 | 190 | 83 | 623 | 107 | 27 | 68 | 190 | 39 |
| 2 | 970 | 40909 | Central | Major Market | Decaf Irish Cream | Beans | Coffee | Colorado | Decaf | 100 | 140 | 110 | 240 | 95 | 821 | 139 | 26 | 101 | 234 | 38 |
| 3 | 303 | 40909 | Central | Major Market | Green Tea | Leaves | Tea | Colorado | Regular | 30 | 50 | 30 | 80 | 44 | 623 | 56 | 14 | 30 | 100 | 26 |
| 4 | 303 | 40909 | Central | Major Market | Caffe Mocha | Beans | Espresso | Colorado | Regular | 60 | 90 | 70 | 150 | 54 | 456 | 80 | 15 | 54 | 134 | 26 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4243 | 206 | 41609 | West | Small Market | Caffe Latte | Beans | Espresso | Washington | Regular | 20 | 30 | 20 | 50 | 24 | 567 | 32 | 7 | 19 | 60 | 19 |
| 4244 | 509 | 41609 | West | Small Market | Caffe Mocha | Beans | Espresso | Washington | Regular | 60 | 80 | 30 | 140 | 65 | 403 | 80 | 24 | 34 | 155 | 57 |
| 4245 | 360 | 41609 | West | Small Market | Decaf Espresso | Beans | Espresso | Washington | Decaf | 70 | 100 | 60 | 170 | 80 | 1079 | 96 | 24 | 76 | 188 | 45 |
| 4246 | 360 | 41609 | West | Small Market | Colombian | Beans | Coffee | Washington | Regular | 80 | 120 | 80 | 200 | 72 | 461 | 104 | 23 | 86 | 188 | 46 |
| 4247 | 206 | 41609 | West | Small Market | Decaf Irish Cream | Beans | Coffee | Washington | Decaf | 120 | 170 | 50 | 290 | 105 | 716 | 145 | 95 | 30 | 266 | 125 |
4248 rows × 20 columns
print(df['Market Size'].nunique())
2
print(df['Product'].count())
4248
m= df['Product'].tolist()
print(m)
['Amaretto', 'Colombian', 'Decaf Irish Cream', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Earl Grey', 'Decaf Espresso', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Earl Grey', 'Decaf Espresso', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Darjeeling', 'Green Tea', 'Decaf Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Decaf Espresso', 'Caffe Mocha', 'Decaf Irish Cream', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Amaretto', 'Colombian', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Mint', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Caffe Latte', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Caffe Latte', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Caffe Latte', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Caffe Latte', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Colombian', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Green Tea', 'Decaf Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Decaf Espresso', 'Caffe Mocha', 'Decaf Irish Cream', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Amaretto', 'Colombian', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Mint', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Caffe Latte', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Caffe Latte', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Colombian', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Green Tea', 'Decaf Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Decaf Espresso', 'Caffe Mocha', 'Decaf Irish Cream', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Regular Espresso', 'Caffe Mocha', 'Amaretto', 'Colombian', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Mint', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Decaf Irish Cream', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Decaf Irish Cream', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Decaf Irish Cream', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Decaf Irish Cream', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Colombian', 'Amaretto', 'Colombian', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Colombian', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Amaretto', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Amaretto', 'Colombian', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Mint', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Decaf 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'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Amaretto', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Amaretto', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Mocha', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Amaretto', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Amaretto', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Amaretto', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Chamomile', 'Lemon', 'Darjeeling', 'Earl Grey', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Mint', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Lemon', 'Darjeeling', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Amaretto', 'Colombian', 'Lemon', 'Mint', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Caffe Mocha', 'Regular Espresso', 'Colombian', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Chamomile', 'Lemon', 'Colombian', 'Decaf Irish Cream', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Mint', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Amaretto', 'Colombian', 'Decaf Irish Cream', 'Darjeeling', 'Earl Grey', 'Green Tea', 'Chamomile', 'Lemon', 'Caffe Latte', 'Caffe Mocha', 'Decaf Espresso', 'Colombian', 'Decaf Irish Cream']
print(df['State'].count())
4248
d2 = df['Product Type'].value_counts()
d2
Espresso 1176 Coffee 1056 Herbal Tea 1056 Tea 960 Name: Product Type, dtype: int64
#plt.bar(d2.index,d2,color='red')
plt.plot(d2.index,d2,color='green',marker='o')
plt.xlabel('product type')
plt.ylabel('product count')
plt.show()
#d8 = df['State'].value_counts()
#d8
h = Counter
d5 = df[df['Product'] == 'Regular Espresso']
d5.shape
(72, 20)
import seaborn as sns
sns.scatterplot(x = d5['Profit'],y= d5['Sales'],color="maroon")
plt.show()
d7 = df.groupby('Market')['Profit'].sum()
d7
Market Central 93852 East 59217 South 32478 West 73996 Name: Profit, dtype: int64
plt.bar(d7.index,d7,color = 'red',label='Profit')
plt.xlabel('profit')
plt.ylabel('market')
#plt.xticks(d7.index,d7,rotation=90)
plt.show()
d9 = df.groupby('Product')['Sales'].mean()
d9
plt.bar(d9.index,d9,color = 'lightblue')
plt.plot(d9.index,d9,color = 'maroon',marker = 'o')
plt.xlabel('Product')
plt.ylabel('Sales')
plt.legend()
plt.title('Product vs Sales mean')
plt.xticks(rotation =60)
plt.show()
No handles with labels found to put in legend.
dfg = df.groupby('Market')['Budget Sales','Sales'].mean()
dfg
#dfg.shape
<ipython-input-29-56b6c924d30f>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.
dfg = df.groupby('Market')['Budget Sales','Sales'].mean()
| Budget Sales | Sales | |
|---|---|---|
| Market | ||
| Central | 186.279762 | 197.206101 |
| East | 177.815315 | 201.099099 |
| South | 146.130952 | 154.651786 |
| West | 178.348214 | 202.577381 |
#plt.plot(kind='Bar',stack=True,color='maroon')
#plt.bar(dfg.index,dfg,label='budget sales vs sales')
#plt.title('market wise budget sales and sales distribution')
#plt.show()
df11 = df[(df['State'] == 'California') & (df['Product'] == 'Earl Grey')]
df11.shape
df11
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 140 | 650 | 40909 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 30 | 60 | 60 | 90 | 54 | 456 | 80 | 15 | 54 | 134 | 26 |
| 317 | 858 | 40940 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 50 | 70 | 60 | 120 | 77 | 449 | 112 | 21 | 80 | 189 | 32 |
| 495 | 951 | 40969 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 50 | 80 | 70 | 130 | 81 | 441 | 117 | 22 | 84 | 198 | 33 |
| 674 | 530 | 41000 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 50 | 80 | 70 | 130 | 81 | 460 | 117 | 22 | 83 | 198 | 34 |
| 851 | 530 | 41030 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 50 | 90 | 80 | 140 | 86 | 499 | 124 | 24 | 89 | 210 | 35 |
| 1027 | 415 | 41061 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 60 | 90 | 80 | 150 | 94 | 562 | 136 | 26 | 98 | 230 | 38 |
| 1201 | 831 | 41091 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 60 | 90 | 80 | 150 | 94 | 608 | 136 | 26 | 98 | 230 | 38 |
| 1378 | 310 | 41122 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 60 | 100 | 90 | 160 | 100 | 598 | 145 | 28 | 105 | 245 | 40 |
| 1555 | 714 | 41153 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 50 | 80 | 70 | 130 | 81 | 588 | 118 | 22 | 84 | 199 | 34 |
| 1730 | 209 | 41183 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 30 | 50 | 50 | 80 | 76 | 580 | 111 | 21 | 79 | 187 | 32 |
| 1907 | 650 | 41214 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 30 | 50 | 40 | 80 | 50 | 589 | 73 | 14 | 47 | 123 | 26 |
| 2084 | 619 | 41244 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 30 | 60 | 50 | 90 | 54 | 601 | 79 | 15 | 53 | 133 | 26 |
| 2261 | 707 | 41275 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 30 | 60 | 60 | 90 | 54 | 456 | 80 | 15 | 84 | 150 | 27 |
| 2438 | 707 | 41306 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 50 | 70 | 60 | 120 | 77 | 449 | 112 | 21 | 119 | 201 | 32 |
| 2615 | 916 | 41334 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 50 | 80 | 70 | 130 | 81 | 441 | 117 | 22 | 125 | 211 | 33 |
| 2792 | 707 | 41365 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 50 | 80 | 70 | 130 | 81 | 460 | 117 | 22 | 123 | 211 | 34 |
| 2969 | 562 | 41395 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 50 | 90 | 80 | 140 | 86 | 499 | 124 | 24 | 123 | 208 | 33 |
| 3146 | 925 | 41426 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 60 | 90 | 80 | 150 | 94 | 562 | 136 | 26 | 135 | 228 | 35 |
| 3319 | 707 | 41456 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 60 | 90 | 80 | 150 | 94 | 608 | 136 | 26 | 135 | 228 | 35 |
| 3497 | 760 | 41487 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 60 | 100 | 90 | 160 | 100 | 598 | 145 | 28 | 145 | 243 | 37 |
| 3674 | 650 | 41518 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 50 | 80 | 70 | 130 | 81 | 588 | 118 | 22 | 125 | 212 | 34 |
| 3851 | 626 | 41548 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 30 | 50 | 50 | 80 | 76 | 580 | 111 | 21 | 117 | 199 | 32 |
| 4028 | 760 | 41579 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 30 | 50 | 40 | 80 | 50 | 589 | 73 | 14 | 70 | 131 | 26 |
| 4205 | 408 | 41609 | West | Major Market | Earl Grey | Leaves | Tea | California | Regular | 30 | 60 | 50 | 90 | 54 | 601 | 79 | 15 | 79 | 142 | 26 |
sns.scatterplot(x = df11['Budget Sales'],y = df11['Budget Profit'],color="green")
plt.title('Budget Profit-Budget sales for product earl grey for california state')
plt.xlabel('Budget Profit')
plt.ylabel('Budget Sales')
plt.show()
plt.hist(x = df['Total Expenses'],bins='auto',label='frequency',color='tan')
plt.show()
dfff = df[df['Sales'] > 400]
dfff
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 13 | 309 | 40909 | Central | Major Market | Caffe Mocha | Beans | Espresso | Illinois | Regular | 270 | 370 | 260 | 640 | 234 | 1310 | 312 | 77 | 203 | 546 | 109 |
| 14 | 630 | 40909 | Central | Major Market | Decaf Espresso | Beans | Espresso | Illinois | Decaf | 260 | 270 | 180 | 530 | 228 | 1459 | 228 | 63 | 140 | 456 | 88 |
| 25 | 641 | 40909 | Central | Small Market | Chamomile | Leaves | Herbal Tea | Iowa | Decaf | 200 | 280 | 200 | 480 | 234 | 1310 | 312 | 77 | 202 | 546 | 110 |
| 27 | 712 | 40909 | Central | Small Market | Darjeeling | Leaves | Tea | Iowa | Regular | 190 | 210 | 140 | 400 | 228 | 1459 | 228 | 63 | 141 | 456 | 87 |
| 28 | 319 | 40909 | Central | Small Market | Earl Grey | Leaves | Tea | Iowa | Regular | 210 | 270 | 160 | 480 | 245 | 1419 | 301 | 93 | 175 | 546 | 126 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4212 | 209 | 41609 | West | Major Market | Decaf Espresso | Beans | Espresso | California | Decaf | 240 | 310 | 210 | 550 | 247 | 1744 | 329 | 81 | 321 | 614 | 113 |
| 4214 | 530 | 41609 | West | Major Market | Colombian | Beans | Coffee | California | Regular | 330 | 500 | 350 | 830 | 279 | 2642 | 420 | 97 | 402 | 745 | 149 |
| 4216 | 775 | 41609 | West | Small Market | Darjeeling | Leaves | Tea | Nevada | Regular | 180 | 240 | 150 | 420 | 247 | 1744 | 329 | 81 | 319 | 614 | 114 |
| 4217 | 775 | 41609 | West | Small Market | Earl Grey | Leaves | Tea | Nevada | Regular | 180 | 180 | 100 | 360 | 250 | 1820 | 251 | 70 | 233 | 534 | 94 |
| 4220 | 702 | 41609 | West | Small Market | Lemon | Leaves | Herbal Tea | Nevada | Decaf | 210 | 280 | 170 | 490 | 224 | 1191 | 310 | 73 | 288 | 569 | 116 |
418 rows × 20 columns
dfff['COGS']
13 234
14 228
25 234
27 228
28 245
...
4212 247
4214 279
4216 247
4217 250
4220 224
Name: COGS, Length: 418, dtype: int64
print(dfff['COGS'])
print(len(dfff['COGS']))
Q0 = np.percentile(dfff['COGS'], 0)
Q1 = np.percentile(dfff['COGS'], 25)
Q2 = np.percentile(dfff['COGS'], 50)
Q3 = np.percentile(dfff['COGS'], 75)
Q4 = np.percentile(dfff['COGS'], 100)
IQR = Q3 - Q1
print('Q0',Q0)
print('Q1',Q1) # 1st quartile
print('Q2',Q2) # meadin
print('Q3',Q3) # 3rd quartile
print('Q4',Q4)
print('IQR',IQR)
13 234
14 228
25 234
27 228
28 245
...
4212 247
4214 279
4216 247
4217 250
4220 224
Name: COGS, Length: 418, dtype: int64
418
Q0 52.0
Q1 228.0
Q2 247.0
Q3 265.0
Q4 364.0
IQR 37.0
df
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 719 | 40909 | Central | Major Market | Amaretto | Beans | Coffee | Colorado | Regular | 90 | 130 | 100 | 220 | 89 | 777 | 130 | 24 | 94 | 219 | 36 |
| 1 | 970 | 40909 | Central | Major Market | Colombian | Beans | Coffee | Colorado | Regular | 80 | 110 | 80 | 190 | 83 | 623 | 107 | 27 | 68 | 190 | 39 |
| 2 | 970 | 40909 | Central | Major Market | Decaf Irish Cream | Beans | Coffee | Colorado | Decaf | 100 | 140 | 110 | 240 | 95 | 821 | 139 | 26 | 101 | 234 | 38 |
| 3 | 303 | 40909 | Central | Major Market | Green Tea | Leaves | Tea | Colorado | Regular | 30 | 50 | 30 | 80 | 44 | 623 | 56 | 14 | 30 | 100 | 26 |
| 4 | 303 | 40909 | Central | Major Market | Caffe Mocha | Beans | Espresso | Colorado | Regular | 60 | 90 | 70 | 150 | 54 | 456 | 80 | 15 | 54 | 134 | 26 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4243 | 206 | 41609 | West | Small Market | Caffe Latte | Beans | Espresso | Washington | Regular | 20 | 30 | 20 | 50 | 24 | 567 | 32 | 7 | 19 | 60 | 19 |
| 4244 | 509 | 41609 | West | Small Market | Caffe Mocha | Beans | Espresso | Washington | Regular | 60 | 80 | 30 | 140 | 65 | 403 | 80 | 24 | 34 | 155 | 57 |
| 4245 | 360 | 41609 | West | Small Market | Decaf Espresso | Beans | Espresso | Washington | Decaf | 70 | 100 | 60 | 170 | 80 | 1079 | 96 | 24 | 76 | 188 | 45 |
| 4246 | 360 | 41609 | West | Small Market | Colombian | Beans | Coffee | Washington | Regular | 80 | 120 | 80 | 200 | 72 | 461 | 104 | 23 | 86 | 188 | 46 |
| 4247 | 206 | 41609 | West | Small Market | Decaf Irish Cream | Beans | Coffee | Washington | Decaf | 120 | 170 | 50 | 290 | 105 | 716 | 145 | 95 | 30 | 266 | 125 |
4248 rows × 20 columns
dffr = df[(df['Product Type']== 'Espresso') & (df['Type'] == 'Decaf')]
dffr
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 5 | 720 | 40909 | Central | Major Market | Decaf Espresso | Beans | Espresso | Colorado | Decaf | 80 | 130 | 80 | 210 | 72 | 558 | 108 | 23 | 53 | 180 | 55 |
| 14 | 630 | 40909 | Central | Major Market | Decaf Espresso | Beans | Espresso | Illinois | Decaf | 260 | 270 | 180 | 530 | 228 | 1459 | 228 | 63 | 140 | 456 | 88 |
| 24 | 563 | 40909 | Central | Small Market | Decaf Espresso | Beans | Espresso | Iowa | Decaf | 10 | 40 | 30 | 50 | 17 | 777 | 26 | 4 | 10 | 43 | 16 |
| 33 | 660 | 40909 | Central | Small Market | Decaf Espresso | Beans | Espresso | Missouri | Decaf | 50 | 80 | 60 | 130 | 46 | 777 | 68 | 12 | 45 | 114 | 23 |
| 42 | 614 | 40909 | Central | Major Market | Decaf Espresso | Beans | Espresso | Ohio | Decaf | 60 | 90 | 40 | 150 | 58 | 338 | 72 | 22 | 16 | 130 | 56 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4200 | 435 | 41609 | West | Small Market | Decaf Espresso | Beans | Espresso | Utah | Decaf | 40 | 70 | 50 | 110 | 50 | 898 | 73 | 14 | 70 | 131 | 26 |
| 4212 | 209 | 41609 | West | Major Market | Decaf Espresso | Beans | Espresso | California | Decaf | 240 | 310 | 210 | 550 | 247 | 1744 | 329 | 81 | 321 | 614 | 113 |
| 4224 | 775 | 41609 | West | Small Market | Decaf Espresso | Beans | Espresso | Nevada | Decaf | 20 | 30 | 20 | 50 | 23 | 807 | 35 | 6 | 25 | 62 | 18 |
| 4234 | 971 | 41609 | West | Small Market | Decaf Espresso | Beans | Espresso | Oregon | Decaf | 150 | 140 | 80 | 290 | 153 | 1319 | 153 | 42 | 129 | 326 | 66 |
| 4245 | 360 | 41609 | West | Small Market | Decaf Espresso | Beans | Espresso | Washington | Decaf | 70 | 100 | 60 | 170 | 80 | 1079 | 96 | 24 | 76 | 188 | 45 |
408 rows × 20 columns
print(dffr['Budget Profit'])
print(len(dffr['Budget Profit']))
Q0 = np.percentile(dffr['Budget Profit'], 0)
Q1 = np.percentile(dffr['Budget Profit'], 25)
Q2 = np.percentile(dffr['Budget Profit'], 50)
Q3 = np.percentile(dffr['Budget Profit'], 75)
Q4 = np.percentile(dffr['Budget Profit'], 100)
IQR = Q3 - Q1
print('Q0',Q0)
print('Q1',Q1) # 1st quartile
print('Q2',Q2) # meadin
print('Q3',Q3) # 3rd quartile
print('Q4',Q4)
print('IQR',IQR)
5 80
14 180
24 30
33 60
42 40
...
4200 50
4212 210
4224 20
4234 80
4245 60
Name: Budget Profit, Length: 408, dtype: int64
408
Q0 0.0
Q1 40.0
Q2 50.0
Q3 82.5
Q4 300.0
IQR 42.5
sales_min = df['Sales'].min()
print(sales_min)
profit_min = df['Profit'].min()
print(profit_min)
total_expense = df['Total Expenses'].max()
print(total_expense)
17 -638 190
d34 = df[(df['Sales']==17)| (df['Profit']==-638)|(df['Total Expenses']==190)]
d34
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 154 | 702 | 40909 | West | Small Market | Green Tea | Leaves | Tea | Nevada | Regular | 160 | -160 | -240 | 0 | 245 | 1419 | -245 | 93 | -354 | 17 | 126 |
| 1385 | 775 | 41122 | West | Small Market | Chamomile | Leaves | Herbal Tea | Nevada | Decaf | 140 | 220 | 70 | 360 | 173 | 927 | 239 | 156 | 49 | 412 | 190 |
| 3687 | 775 | 41518 | West | Small Market | Green Tea | Leaves | Tea | Nevada | Regular | 190 | -190 | -280 | 0 | 302 | 6413 | -302 | 114 | -638 | 20 | 147 |
d34[['State','Market','Product','Type','Sales','Profit','Total Expenses']]
| State | Market | Product | Type | Sales | Profit | Total Expenses | |
|---|---|---|---|---|---|---|---|
| 154 | Nevada | West | Green Tea | Regular | 17 | -354 | 126 |
| 1385 | Nevada | West | Chamomile | Decaf | 412 | 49 | 190 |
| 3687 | Nevada | West | Green Tea | Regular | 20 | -638 | 147 |
sales_max = df['Sales'].max()
print(sales_max)
profit_max = df['Profit'].max()
print(profit_max)
total_expense = df['Total Expenses'].min()
print(total_expense)
912 778 10
d33 = df[(df['Sales']==912) | (df['Profit']==778) | (df['Total Expenses']==10)]
#d33.shape
d33
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1154 | 845 | 41091 | East | Major Market | Colombian | Beans | Coffee | New York | Regular | 340 | 520 | 370 | 860 | 364 | 2654 | 548 | 127 | 370 | 912 | 178 |
| 1196 | 707 | 41091 | West | Major Market | Colombian | Beans | Coffee | California | Regular | 450 | 690 | 530 | 1140 | 364 | 2654 | 548 | 127 | 370 | 912 | 178 |
| 2910 | 603 | 41395 | East | Small Market | Green Tea | Leaves | Tea | New Hampshire | Regular | 0 | 40 | 30 | 40 | 0 | 645 | 43 | 0 | 44 | 43 | 10 |
| 3087 | 603 | 41426 | East | Small Market | Green Tea | Leaves | Tea | New Hampshire | Regular | 0 | 40 | 30 | 40 | 0 | 602 | 43 | 0 | 44 | 43 | 10 |
| 3263 | 978 | 41456 | East | Major Market | Colombian | Beans | Coffee | Massachusetts | Regular | 40 | 590 | 560 | 630 | 52 | -1493 | 613 | 17 | 778 | 659 | 46 |
| 3339 | 775 | 41456 | West | Small Market | Caffe Latte | Beans | Espresso | Nevada | Regular | 0 | 40 | 30 | 40 | 0 | 559 | 43 | 0 | 44 | 43 | 10 |
| 3516 | 775 | 41487 | West | Small Market | Caffe Latte | Beans | Espresso | Nevada | Regular | 0 | 40 | 30 | 40 | 0 | 516 | 43 | 0 | 44 | 43 | 10 |
moto = pd.concat([df['Product'],df['State'],df['Market'],df['Total Expenses']],axis=1)
moto
#moto.shape
| Product | State | Market | Total Expenses | |
|---|---|---|---|---|
| 0 | Amaretto | Colorado | Central | 36 |
| 1 | Colombian | Colorado | Central | 39 |
| 2 | Decaf Irish Cream | Colorado | Central | 38 |
| 3 | Green Tea | Colorado | Central | 26 |
| 4 | Caffe Mocha | Colorado | Central | 26 |
| ... | ... | ... | ... | ... |
| 4243 | Caffe Latte | Washington | West | 19 |
| 4244 | Caffe Mocha | Washington | West | 57 |
| 4245 | Decaf Espresso | Washington | West | 45 |
| 4246 | Colombian | Washington | West | 46 |
| 4247 | Decaf Irish Cream | Washington | West | 125 |
4248 rows × 4 columns
d33[['State','Market','Product','Type','Sales','Profit','Total Expenses']]
| State | Market | Product | Type | Sales | Profit | Total Expenses | |
|---|---|---|---|---|---|---|---|
| 1154 | New York | East | Colombian | Regular | 912 | 370 | 178 |
| 1196 | California | West | Colombian | Regular | 912 | 370 | 178 |
| 2910 | New Hampshire | East | Green Tea | Regular | 43 | 44 | 10 |
| 3087 | New Hampshire | East | Green Tea | Regular | 43 | 44 | 10 |
| 3263 | Massachusetts | East | Colombian | Regular | 659 | 778 | 46 |
| 3339 | Nevada | West | Caffe Latte | Regular | 43 | 44 | 10 |
| 3516 | Nevada | West | Caffe Latte | Regular | 43 | 44 | 10 |
dh = df.groupby(['Market','Type'])['Budget Profit'].sum()
dh
Market Type
Central Decaf 43880
Regular 48700
East Decaf 10800
Regular 45980
South Decaf 16320
Regular 18720
West Decaf 36240
Regular 38120
Name: Budget Profit, dtype: int64
dh.plot(kind='bar',color='maroon')
plt.title('Market and Type wise Budget Profit')
plt.xlabel('Budget Profit')
plt.ylabel('Market and Type')
plt.legend()
plt.xticks(rotation=45)
plt.show()
dc = df.groupby(['Product Type','State'])['Sales'].mean()
dc
Product Type State
Coffee California 343.027778
Colorado 229.819444
Connecticut 288.458333
Florida 249.458333
Illinois 376.270833
...
Tea Ohio 249.458333
Oregon 185.583333
Utah 92.638889
Washington 135.888889
Wisconsin 131.375000
Name: Sales, Length: 76, dtype: float64
dc.plot(kind='area',color='green')
plt.title('Product type vs state sales')
plt.xlabel('product type')
plt.ylabel('sales mean')
plt.xticks(rotation=60)
plt.show()
12#q
d14 = df[df['State']=='Oregon']
d14.shape
(264, 20)
plt.hist(d14['Profit'],bins='auto',color='red',label='Profit')
plt.hist(d14['Budget Profit'],bins='auto',label='green')
plt.legend()
plt.show()
#q13
d15 = df[(df['Market']=='East') & (df['Product']=='Caffe Mocha')]
d15
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 59 | 860 | 40909 | East | Small Market | Caffe Mocha | Beans | Espresso | Connecticut | Regular | 70 | 100 | 20 | 170 | 75 | 522 | 105 | 68 | 7 | 180 | 98 |
| 66 | 239 | 40909 | East | Major Market | Caffe Mocha | Beans | Espresso | Florida | Regular | 70 | 110 | 30 | 180 | 79 | 551 | 111 | 72 | 9 | 190 | 102 |
| 75 | 781 | 40909 | East | Major Market | Caffe Mocha | Beans | Espresso | Massachusetts | Regular | 60 | 60 | -10 | 120 | 63 | 435 | 63 | 57 | -23 | 126 | 86 |
| 81 | 603 | 40909 | East | Small Market | Caffe Mocha | Beans | Espresso | New Hampshire | Regular | 30 | 50 | 0 | 80 | 37 | 261 | 53 | 34 | -10 | 90 | 63 |
| 89 | 315 | 40909 | East | Major Market | Caffe Mocha | Beans | Espresso | New York | Regular | 100 | -50 | -170 | 50 | 105 | 725 | -44 | 95 | -172 | 61 | 128 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4131 | 203 | 41609 | East | Small Market | Caffe Mocha | Beans | Espresso | Connecticut | Regular | 50 | 70 | 10 | 120 | 55 | 627 | 76 | 49 | -3 | 140 | 78 |
| 4139 | 305 | 41609 | East | Major Market | Caffe Mocha | Beans | Espresso | Florida | Regular | 100 | 140 | 30 | 240 | 105 | 716 | 145 | 95 | 31 | 266 | 124 |
| 4146 | 978 | 41609 | East | Major Market | Caffe Mocha | Beans | Espresso | Massachusetts | Regular | 40 | 50 | -10 | 90 | 47 | 571 | 52 | 42 | -30 | 106 | 72 |
| 4152 | 603 | 41609 | East | Small Market | Caffe Mocha | Beans | Espresso | New Hampshire | Regular | 40 | 70 | 10 | 110 | 49 | 335 | 69 | 44 | -6 | 126 | 73 |
| 4161 | 845 | 41609 | East | Major Market | Caffe Mocha | Beans | Espresso | New York | Regular | 130 | -70 | -210 | 60 | 135 | 3641 | -69 | 122 | -332 | 70 | 155 |
120 rows × 20 columns
#Q13
plt.hist(d15['COGS'],bins='auto',color='blue',label='COGS')
plt.hist(d15['Budget COGS'],bins='auto',color='green',label='Budget COGS')
plt.legend()
plt.show()
#Q14
d18 = df[((df['Market']=='East')|(df['Market']=='Central')) & (df['Type']=='Decaf')]
d18
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 970 | 40909 | Central | Major Market | Decaf Irish Cream | Beans | Coffee | Colorado | Decaf | 100 | 140 | 110 | 240 | 95 | 821 | 139 | 26 | 101 | 234 | 38 |
| 5 | 720 | 40909 | Central | Major Market | Decaf Espresso | Beans | Espresso | Colorado | Decaf | 80 | 130 | 80 | 210 | 72 | 558 | 108 | 23 | 53 | 180 | 55 |
| 6 | 970 | 40909 | Central | Major Market | Chamomile | Leaves | Herbal Tea | Colorado | Decaf | 140 | 160 | 110 | 300 | 170 | 1091 | 171 | 47 | 99 | 341 | 72 |
| 7 | 719 | 40909 | Central | Major Market | Lemon | Leaves | Herbal Tea | Colorado | Decaf | 50 | 80 | 20 | 130 | 63 | 435 | 87 | 57 | 0 | 150 | 87 |
| 8 | 970 | 40909 | Central | Major Market | Mint | Leaves | Herbal Tea | Colorado | Decaf | 50 | 70 | 40 | 120 | 60 | 336 | 80 | 19 | 33 | 140 | 47 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4142 | 727 | 41609 | East | Major Market | Decaf Irish Cream | Beans | Coffee | Florida | Decaf | 120 | 160 | 110 | 280 | 134 | 690 | 186 | 41 | 181 | 341 | 64 |
| 4143 | 774 | 41609 | East | Major Market | Lemon | Leaves | Herbal Tea | Massachusetts | Decaf | 30 | 40 | 10 | 70 | 46 | 422 | 57 | 17 | 10 | 110 | 50 |
| 4149 | 603 | 41609 | East | Small Market | Lemon | Leaves | Herbal Tea | New Hampshire | Decaf | 20 | 40 | 10 | 60 | 39 | 250 | 49 | 14 | 3 | 94 | 47 |
| 4156 | 631 | 41609 | East | Major Market | Lemon | Leaves | Herbal Tea | New York | Decaf | 220 | 320 | 210 | 540 | 294 | 1727 | 453 | 111 | 459 | 796 | 144 |
| 4157 | 716 | 41609 | East | Major Market | Mint | Leaves | Herbal Tea | New York | Decaf | 180 | -80 | -150 | 100 | 241 | 5121 | -93 | 74 | -280 | 158 | 96 |
912 rows × 20 columns
plt.hist(d18['Sales'],bins='auto',color='maroon',label='Sales')
plt.hist(d18['Profit'],bins='auto',color='tan',label='Profit')
plt.legend()
plt.show()
d10 = df.groupby('Market')['Budget Sales'].mean()
d10
Market Central 186.279762 East 177.815315 South 146.130952 West 178.348214 Name: Budget Sales, dtype: float64
d20 = df.groupby('Market')['Sales'].mean()
d20
Market Central 197.206101 East 201.099099 South 154.651786 West 202.577381 Name: Sales, dtype: float64
#Q10
plt.bar(d20.index,d20,color='green')
plt.bar(d10.index,d10,color='maroon')
plt.title('market wise mean of budget sales and sales ')
plt.xlabel('Market')
plt.ylabel('sales and budget sales mean')
plt.show()
###Depict Product Type and State wise mean of Sales on a area chart<br>
#24) Depict Product and Type wise sum of Budget COGS on a line Chart<br>
#25) Depict Product and Type wise sum of Budget Profit and Budget Sales on a line Chart<br>
#26) Depict Market and Product Type based mean of Sales on abar chart and on a stacked bar chart separately<br>
#27) Find State and Market Size based mean of Sales and COGS where Market is not Central and Profit is over 350. Depict the result on a line chart, stacked bar and area chart separately<br>
#28) Find Product and Market Size based mean of Budget COGS and Inventory where Type is Regular and neither product or its Type is Espresso. Depict the result on a line chart, stacked bar and area ch
df22 = df[(df['Market']!='Central')| (df['Profit']>350)]
df22
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 56 | 475 | 40909 | East | Small Market | Darjeeling | Leaves | Tea | Connecticut | Regular | 50 | 80 | 60 | 130 | 50 | 821 | 73 | 14 | 47 | 123 | 26 |
| 57 | 475 | 40909 | East | Small Market | Green Tea | Leaves | Tea | Connecticut | Regular | 40 | 60 | 40 | 100 | 38 | 777 | 56 | 10 | 35 | 94 | 21 |
| 58 | 475 | 40909 | East | Small Market | Decaf Espresso | Beans | Espresso | Connecticut | Decaf | 50 | 70 | 40 | 120 | 55 | 312 | 75 | 18 | 30 | 130 | 45 |
| 59 | 860 | 40909 | East | Small Market | Caffe Mocha | Beans | Espresso | Connecticut | Regular | 70 | 100 | 20 | 170 | 75 | 522 | 105 | 68 | 7 | 180 | 98 |
| 60 | 475 | 40909 | East | Small Market | Colombian | Beans | Coffee | Connecticut | Regular | 110 | 180 | 130 | 290 | 124 | 961 | 186 | 40 | 115 | 310 | 71 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4243 | 206 | 41609 | West | Small Market | Caffe Latte | Beans | Espresso | Washington | Regular | 20 | 30 | 20 | 50 | 24 | 567 | 32 | 7 | 19 | 60 | 19 |
| 4244 | 509 | 41609 | West | Small Market | Caffe Mocha | Beans | Espresso | Washington | Regular | 60 | 80 | 30 | 140 | 65 | 403 | 80 | 24 | 34 | 155 | 57 |
| 4245 | 360 | 41609 | West | Small Market | Decaf Espresso | Beans | Espresso | Washington | Decaf | 70 | 100 | 60 | 170 | 80 | 1079 | 96 | 24 | 76 | 188 | 45 |
| 4246 | 360 | 41609 | West | Small Market | Colombian | Beans | Coffee | Washington | Regular | 80 | 120 | 80 | 200 | 72 | 461 | 104 | 23 | 86 | 188 | 46 |
| 4247 | 206 | 41609 | West | Small Market | Decaf Irish Cream | Beans | Coffee | Washington | Decaf | 120 | 170 | 50 | 290 | 105 | 716 | 145 | 95 | 30 | 266 | 125 |
2908 rows × 20 columns
d23 = df22.groupby('Market Size')['Sales'].mean()
d23
Market Size Major Market 271.202970 Small Market 149.397787 Name: Sales, dtype: float64
d24 = df22.groupby('Market Size')['COGS'].mean()
d24
Market Size Major Market 120.265347 Small Market 66.000000 Name: COGS, dtype: float64
d23.plot(kind='area',color='maroon')
d24.plot(kind='area',color='yellow')
plt.title('area chart of budget and cogs mean based on market size')
plt.xlabel('market size')
plt.ylabel('budget and cogs mean')
plt.show()
plt.bar(d23.index,d23,color='maroon')
plt.bar(d24.index,d24,color='red')
plt.show()
d28 = df.groupby(['Market','Product Type'])['Sales'].mean()
d28
Market Product Type
Central Coffee 179.895833
Espresso 207.295139
Herbal Tea 202.047619
Tea 203.500000
East Coffee 337.148810
Espresso 201.654167
Herbal Tea 191.486111
Tea 121.882576
South Coffee 173.223958
Espresso 156.215278
Herbal Tea 133.734375
West Coffee 185.410256
Espresso 194.188889
Herbal Tea 231.692308
Tea 200.611111
Name: Sales, dtype: float64
d28.plot(kind='bar',color='orange')
plt.title('market and product type based sales mean')
plt.ylabel('sales mean')
plt.show()
dt = df.groupby('Market Size')['Sales'].mean()
dt
Market Size Major Market 249.495892 Small Market 155.137579 Name: Sales, dtype: float64
dm = df.groupby('Product Type')['Sales'].mean()
dm
Product Type Coffee 205.329545 Espresso 189.622449 Herbal Tea 196.225379 Tea 179.971875 Name: Sales, dtype: float64
plt.bar(dt.index,dt,color='yellow')
plt.bar(dm.index,dm,color='green')
plt.xlabel('Product Type and Market Size')
plt.ylabel('sales mean')
plt.xticks(rotation=60)
plt.show()
sot = df.groupby(['Product','Type'])['Budget Sales','Budget Profit'].sum()
sot
<ipython-input-71-52431f5cd96a>:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead. sot = df.groupby(['Product','Type'])['Budget Sales','Budget Profit'].sum()
| Budget Sales | Budget Profit | ||
|---|---|---|---|
| Product | Type | ||
| Amaretto | Regular | 27200 | 7020 |
| Caffe Latte | Regular | 30540 | 10100 |
| Caffe Mocha | Regular | 84600 | 23280 |
| Chamomile | Decaf | 63840 | 24920 |
| Colombian | Regular | 134380 | 57800 |
| Darjeeling | Regular | 57360 | 22860 |
| Decaf Espresso | Decaf | 75720 | 29460 |
| Decaf Irish Cream | Decaf | 67040 | 19060 |
| Earl Grey | Regular | 50900 | 17600 |
| Green Tea | Regular | 25340 | 3800 |
| Lemon | Decaf | 78300 | 27020 |
| Mint | Decaf | 28320 | 6780 |
| Regular Espresso | Regular | 22620 | 9060 |
sot.plot(kind='line',color='green',marker='^')
plt.title('Product and Type wise budget sales and profit sum')
plt.xlabel('Product and Type')
plt.ylabel('Budget sales and Budget profit')
plt.xticks(rotation=90)
plt.show()
sit = df.groupby(['Product','Type'])['Budget COGS'].sum()
sit
Product Type Amaretto Regular 13420 Caffe Latte Regular 12820 Caffe Mocha Regular 36820 Chamomile Decaf 25360 Colombian Regular 51240 Darjeeling Regular 22880 Decaf Espresso Decaf 31660 Decaf Irish Cream Decaf 32220 Earl Grey Regular 21040 Green Tea Regular 13220 Lemon Decaf 32560 Mint Decaf 15140 Regular Espresso Regular 9500 Name: Budget COGS, dtype: int64
sit.plot(kind='line',color='green',marker='^')
plt.title('Product and Type wise budget COGS sum')
plt.xlabel('Product and Type')
plt.ylabel('Budget COGS sum')
plt.xticks(rotation=90)
plt.show()
####SEABORN
Depict Product based on Market Size on a count plot<br>
2) Depict State based on Market on a count plot<br>
3) Depict the Product Type vs Total Expenses on a Violin Plot based on Market<br>
4) Depict Market vs Budget COGS on a boxplot<br>
5) Depict Product vs Margin on a boxplot based on Market Size<br>
6) Depict the Market vs Budget Sales on a Violin Plot based on Product Type where product is Colombina or Darjeeling<br>
7) Depict the Product vs Inventory on a Box Plot based on Product Type where State is California or Nevada<br>
8) Generate Pairplot for the Coffee dataset where Type is Regular including fields- Budget Sales, Budget Profit, Budget COGS, Sales, Proift and COGS<br>
9) Generate Pairplot for the Coffee dataset where Profit is over 250 based on Type including fields- Budget Sales, Budget Profit, Budget COGS, Sales, Proift and COGS<br>
10) sns.pairplot(d67)
plt.show()<br>
11) Depict Product vs Sales based on Market on a Strip Plot where Type is Decaf and Market is not Central<br>
12) Depict State vs Budget Profit based on Product Type on a Strip Plot where Market is not Western and Sales >300<br>
13) Depict Market vs Total Expenses based on Type on a Swarm Plot where Sales is over 200 and Market Size is Small<br>
14) Depict correlation values on a heatmap<br>
15) Depict Budget Sales vs Sales on a Joint Plot<br>
16) Depict Total Expenses on Joint plot where Sales>300 or Profit>300 where joint plot kind is scatter<br>
17) Depict Budget COGS on Joint plot where Market is Eastern and Type is Regular where joint plot kind is hex
<br>
18) Depict Marketing on Joint plot where Market is not Central and Sales is over 500 where joint plot kind is kde<br>
d88 = df['Market Size'].value_counts()
d88
Small Market 2544 Major Market 1704 Name: Market Size, dtype: int64
d89 = df['Product'].value_counts()
d89
Lemon 480 Colombian 480 Caffe Mocha 480 Decaf Espresso 408 Chamomile 384 Darjeeling 384 Decaf Irish Cream 384 Earl Grey 288 Green Tea 288 Caffe Latte 216 Amaretto 192 Mint 192 Regular Espresso 72 Name: Product, dtype: int64
#Q1
sns.countplot(df['Product'],hue=df['Market Size'])
plt.title('Product Count based on Market Size')
plt.xlabel('Product')
plt.ylabel('Product Count')
plt.xticks(rotation=90)
#plt.grid()
plt.show()
C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation. warnings.warn(
#Q2
sns.countplot(df['State'],hue=df['Market'])
plt.title('State Count based on Market')
plt.xlabel('State')
plt.ylabel('State Count')
plt.xticks(rotation=90)
#plt.grid()
plt.show()
C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation. warnings.warn(
#Q3) Depict the Product Type vs Total Expenses on a Violin Plot based on Market<br>
sns.violinplot(x='Product Type',y='Total Expenses',data=df,
hue='Market')
plt.show()
#Q4) Depict Market vs Budget COGS on a boxplot<br>
sns.boxplot(df['Market'], y=df['Budget COGS'])
plt.show()
C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation. warnings.warn(
#Q5) Depict Product vs Margin on a boxplot based on Market Size<br>
sns.boxplot(df['Product'], y=df['Margin'],hue=df['Market Size'])
plt.xticks(rotation=90)
plt.show()
C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation. warnings.warn(
#Q6) Depict the Market vs Budget Sales on a Violin Plot based on Product Type where product is Colombina or Darjeeling
d44 = df[(df['Product']=='Colombina') | (df['Product']=='Darjeeling')]
d44
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 9 | 719 | 40909 | Central | Major Market | Darjeeling | Leaves | Tea | Colorado | Regular | 40 | 70 | 20 | 110 | 58 | 338 | 72 | 22 | 17 | 130 | 55 |
| 18 | 217 | 40909 | Central | Major Market | Darjeeling | Leaves | Tea | Illinois | Regular | 40 | 70 | 50 | 110 | 54 | 456 | 80 | 15 | 53 | 134 | 27 |
| 27 | 712 | 40909 | Central | Small Market | Darjeeling | Leaves | Tea | Iowa | Regular | 190 | 210 | 140 | 400 | 228 | 1459 | 228 | 63 | 141 | 456 | 87 |
| 36 | 314 | 40909 | Central | Small Market | Darjeeling | Leaves | Tea | Missouri | Regular | 30 | 40 | 10 | 70 | 35 | 196 | 47 | 11 | 9 | 82 | 38 |
| 46 | 567 | 40909 | Central | Major Market | Darjeeling | Leaves | Tea | Ohio | Regular | 70 | 110 | 60 | 180 | 84 | 651 | 126 | 27 | 68 | 210 | 58 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4192 | 435 | 41609 | West | Small Market | Darjeeling | Leaves | Tea | Utah | Regular | 30 | 50 | 0 | 80 | 49 | 335 | 69 | 44 | -6 | 126 | 73 |
| 4204 | 323 | 41609 | West | Major Market | Darjeeling | Leaves | Tea | California | Regular | 90 | 130 | 110 | 220 | 123 | 959 | 179 | 34 | 199 | 322 | 45 |
| 4216 | 775 | 41609 | West | Small Market | Darjeeling | Leaves | Tea | Nevada | Regular | 180 | 240 | 150 | 420 | 247 | 1744 | 329 | 81 | 319 | 614 | 114 |
| 4227 | 541 | 41609 | West | Small Market | Darjeeling | Leaves | Tea | Oregon | Regular | 20 | 30 | 30 | 50 | 29 | 490 | 44 | 8 | 37 | 78 | 19 |
| 4238 | 253 | 41609 | West | Small Market | Darjeeling | Leaves | Tea | Washington | Regular | 30 | 40 | 20 | 70 | 41 | 320 | 66 | 12 | 45 | 114 | 36 |
384 rows × 20 columns
sns.violinplot(x='Market',y='Budget Sales',data=d44,
hue='Product Type')
plt.show()
#Q7Depict the Product vs Inventory on a Box Plot based on Product Type where State is California or Nevada<
d80 = df[(df['State']=='California')|(df['State']=='Nevada')]
d80
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 133 | 661 | 40909 | West | Major Market | Caffe Latte | Beans | Espresso | California | Regular | 210 | 220 | 150 | 430 | 228 | 1459 | 228 | 63 | 140 | 456 | 88 |
| 134 | 818 | 40909 | West | Major Market | Caffe Mocha | Beans | Espresso | California | Regular | 100 | 140 | 20 | 240 | 105 | 725 | 145 | 95 | 17 | 250 | 128 |
| 135 | 213 | 40909 | West | Major Market | Decaf Espresso | Beans | Espresso | California | Decaf | 220 | 300 | 200 | 520 | 234 | 1310 | 312 | 77 | 203 | 546 | 109 |
| 136 | 510 | 40909 | West | Major Market | Chamomile | Leaves | Herbal Tea | California | Decaf | 80 | 120 | 100 | 200 | 95 | 821 | 139 | 26 | 102 | 234 | 37 |
| 137 | 310 | 40909 | West | Major Market | Lemon | Leaves | Herbal Tea | California | Decaf | 170 | 220 | 160 | 390 | 207 | 965 | 245 | 64 | 160 | 452 | 85 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4222 | 775 | 41609 | West | Small Market | Caffe Latte | Beans | Espresso | Nevada | Regular | 0 | 40 | 30 | 40 | 0 | 344 | 43 | 0 | 46 | 46 | 12 |
| 4223 | 775 | 41609 | West | Small Market | Caffe Mocha | Beans | Espresso | Nevada | Regular | 10 | 20 | 10 | 30 | 16 | 851 | 25 | 4 | 15 | 44 | 15 |
| 4224 | 775 | 41609 | West | Small Market | Decaf Espresso | Beans | Espresso | Nevada | Decaf | 20 | 30 | 20 | 50 | 23 | 807 | 35 | 6 | 25 | 62 | 18 |
| 4225 | 702 | 41609 | West | Small Market | Colombian | Beans | Coffee | Nevada | Regular | 40 | 60 | 20 | 100 | 39 | 250 | 49 | 14 | 1 | 94 | 48 |
| 4226 | 775 | 41609 | West | Small Market | Decaf Irish Cream | Beans | Coffee | Nevada | Decaf | 30 | 50 | 30 | 80 | 31 | 1009 | 38 | 9 | 12 | 74 | 30 |
552 rows × 20 columns
sns.boxplot(d80['Product'], y=d80['Inventory'],hue=df['Product Type'])
plt.xticks(rotation=90)
plt.show()
C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation. warnings.warn(
#Q8) Generate Pairplot for the Coffee dataset where Type is Regular including fields- Budget Sales, Budget Profit, Budget COGS, Sales, Proift and COGS<br
d67 = df[(df['Type']=='Regular')|(df['Product Type']=='Coffee')]
d67
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 719 | 40909 | Central | Major Market | Amaretto | Beans | Coffee | Colorado | Regular | 90 | 130 | 100 | 220 | 89 | 777 | 130 | 24 | 94 | 219 | 36 |
| 1 | 970 | 40909 | Central | Major Market | Colombian | Beans | Coffee | Colorado | Regular | 80 | 110 | 80 | 190 | 83 | 623 | 107 | 27 | 68 | 190 | 39 |
| 2 | 970 | 40909 | Central | Major Market | Decaf Irish Cream | Beans | Coffee | Colorado | Decaf | 100 | 140 | 110 | 240 | 95 | 821 | 139 | 26 | 101 | 234 | 38 |
| 3 | 303 | 40909 | Central | Major Market | Green Tea | Leaves | Tea | Colorado | Regular | 30 | 50 | 30 | 80 | 44 | 623 | 56 | 14 | 30 | 100 | 26 |
| 4 | 303 | 40909 | Central | Major Market | Caffe Mocha | Beans | Espresso | Colorado | Regular | 60 | 90 | 70 | 150 | 54 | 456 | 80 | 15 | 54 | 134 | 26 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4240 | 509 | 41609 | West | Small Market | Green Tea | Leaves | Tea | Washington | Regular | 40 | 50 | -10 | 90 | 55 | 627 | 76 | 49 | -4 | 140 | 79 |
| 4243 | 206 | 41609 | West | Small Market | Caffe Latte | Beans | Espresso | Washington | Regular | 20 | 30 | 20 | 50 | 24 | 567 | 32 | 7 | 19 | 60 | 19 |
| 4244 | 509 | 41609 | West | Small Market | Caffe Mocha | Beans | Espresso | Washington | Regular | 60 | 80 | 30 | 140 | 65 | 403 | 80 | 24 | 34 | 155 | 57 |
| 4246 | 360 | 41609 | West | Small Market | Colombian | Beans | Coffee | Washington | Regular | 80 | 120 | 80 | 200 | 72 | 461 | 104 | 23 | 86 | 188 | 46 |
| 4247 | 206 | 41609 | West | Small Market | Decaf Irish Cream | Beans | Coffee | Washington | Decaf | 120 | 170 | 50 | 290 | 105 | 716 | 145 | 95 | 30 | 266 | 125 |
2784 rows × 20 columns
d67[['Budget Sales','Budget Profit', 'Budget COGS', 'Sales', 'Profit','COGS']]
| Budget Sales | Budget Profit | Budget COGS | Sales | Profit | COGS | |
|---|---|---|---|---|---|---|
| 0 | 220 | 100 | 90 | 219 | 94 | 89 |
| 1 | 190 | 80 | 80 | 190 | 68 | 83 |
| 2 | 240 | 110 | 100 | 234 | 101 | 95 |
| 3 | 80 | 30 | 30 | 100 | 30 | 44 |
| 4 | 150 | 70 | 60 | 134 | 54 | 54 |
| ... | ... | ... | ... | ... | ... | ... |
| 4240 | 90 | -10 | 40 | 140 | -4 | 55 |
| 4243 | 50 | 20 | 20 | 60 | 19 | 24 |
| 4244 | 140 | 30 | 60 | 155 | 34 | 65 |
| 4246 | 200 | 80 | 80 | 188 | 86 | 72 |
| 4247 | 290 | 50 | 120 | 266 | 30 | 105 |
2784 rows × 6 columns
sns.pairplot(d67)
plt.show()
#Q9 Generate Pairplot for the Coffee dataset where Profit is over 250 based on Type including fields- Budget Sales, Budget Profit, Budget COGS, Sales, Proift and COGS<br>
d99 = df[(df['Profit']>250)]
d99
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 76 | 413 | 40909 | East | Major Market | Colombian | Beans | Coffee | Massachusetts | Regular | 60 | 400 | 360 | 460 | 72 | 558 | 422 | 23 | 367 | 494 | 55 |
| 90 | 716 | 40909 | East | Major Market | Colombian | Beans | Coffee | New York | Regular | 260 | 380 | 260 | 640 | 271 | 2101 | 407 | 94 | 262 | 678 | 145 |
| 143 | 559 | 40909 | West | Major Market | Colombian | Beans | Coffee | California | Regular | 340 | 500 | 370 | 840 | 271 | 2101 | 407 | 94 | 262 | 678 | 145 |
| 253 | 774 | 40940 | East | Major Market | Colombian | Beans | Coffee | Massachusetts | Regular | 70 | 370 | 330 | 440 | 75 | 235 | 395 | 24 | 340 | 470 | 55 |
| 265 | 585 | 40940 | East | Major Market | Regular Espresso | Beans | Espresso | New York | Regular | 210 | 350 | 280 | 560 | 228 | 1402 | 352 | 63 | 264 | 580 | 88 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4208 | 661 | 41609 | West | Major Market | Lemon | Leaves | Herbal Tea | California | Decaf | 220 | 260 | 170 | 480 | 241 | 1321 | 284 | 74 | 279 | 559 | 96 |
| 4212 | 209 | 41609 | West | Major Market | Decaf Espresso | Beans | Espresso | California | Decaf | 240 | 310 | 210 | 550 | 247 | 1744 | 329 | 81 | 321 | 614 | 113 |
| 4214 | 530 | 41609 | West | Major Market | Colombian | Beans | Coffee | California | Regular | 330 | 500 | 350 | 830 | 279 | 2642 | 420 | 97 | 402 | 745 | 149 |
| 4216 | 775 | 41609 | West | Small Market | Darjeeling | Leaves | Tea | Nevada | Regular | 180 | 240 | 150 | 420 | 247 | 1744 | 329 | 81 | 319 | 614 | 114 |
| 4220 | 702 | 41609 | West | Small Market | Lemon | Leaves | Herbal Tea | Nevada | Decaf | 210 | 280 | 170 | 490 | 224 | 1191 | 310 | 73 | 288 | 569 | 116 |
181 rows × 20 columns
d99[['Budget Sales', 'Budget Profit', 'Budget COGS', 'Sales', 'Profit', 'COGS']]
| Budget Sales | Budget Profit | Budget COGS | Sales | Profit | COGS | |
|---|---|---|---|---|---|---|
| 76 | 460 | 360 | 60 | 494 | 367 | 72 |
| 90 | 640 | 260 | 260 | 678 | 262 | 271 |
| 143 | 840 | 370 | 340 | 678 | 262 | 271 |
| 253 | 440 | 330 | 70 | 470 | 340 | 75 |
| 265 | 560 | 280 | 210 | 580 | 264 | 228 |
| ... | ... | ... | ... | ... | ... | ... |
| 4208 | 480 | 170 | 220 | 559 | 279 | 241 |
| 4212 | 550 | 210 | 240 | 614 | 321 | 247 |
| 4214 | 830 | 350 | 330 | 745 | 402 | 279 |
| 4216 | 420 | 150 | 180 | 614 | 319 | 247 |
| 4220 | 490 | 170 | 210 | 569 | 288 | 224 |
181 rows × 6 columns
#sns.pairplot(d99)
#plt.show()
#Q10Generate Pairplot for the Coffee dataset where Market is Western or Central based and Product Line is beans based on Type including fields- COGS, Sales, Proift and Total Expenses<br>
d100 = df[(df['Market']=='Western')|(df['Market']=='Central')&(df['Product Line']=='Beans')]
d100
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 719 | 40909 | Central | Major Market | Amaretto | Beans | Coffee | Colorado | Regular | 90 | 130 | 100 | 220 | 89 | 777 | 130 | 24 | 94 | 219 | 36 |
| 1 | 970 | 40909 | Central | Major Market | Colombian | Beans | Coffee | Colorado | Regular | 80 | 110 | 80 | 190 | 83 | 623 | 107 | 27 | 68 | 190 | 39 |
| 2 | 970 | 40909 | Central | Major Market | Decaf Irish Cream | Beans | Coffee | Colorado | Decaf | 100 | 140 | 110 | 240 | 95 | 821 | 139 | 26 | 101 | 234 | 38 |
| 4 | 303 | 40909 | Central | Major Market | Caffe Mocha | Beans | Espresso | Colorado | Regular | 60 | 90 | 70 | 150 | 54 | 456 | 80 | 15 | 54 | 134 | 26 |
| 5 | 720 | 40909 | Central | Major Market | Decaf Espresso | Beans | Espresso | Colorado | Decaf | 80 | 130 | 80 | 210 | 72 | 558 | 108 | 23 | 53 | 180 | 55 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4122 | 262 | 41609 | Central | Small Market | Caffe Mocha | Beans | Espresso | Wisconsin | Regular | 100 | 120 | 80 | 220 | 80 | 1079 | 96 | 24 | 74 | 188 | 46 |
| 4123 | 608 | 41609 | Central | Small Market | Decaf Espresso | Beans | Espresso | Wisconsin | Decaf | 30 | 40 | 30 | 70 | 24 | 567 | 32 | 7 | 19 | 60 | 19 |
| 4124 | 920 | 41609 | Central | Small Market | Amaretto | Beans | Coffee | Wisconsin | Regular | 60 | 80 | 30 | 140 | 65 | 403 | 80 | 24 | 33 | 155 | 58 |
| 4125 | 262 | 41609 | Central | Small Market | Colombian | Beans | Coffee | Wisconsin | Regular | 100 | 140 | 30 | 240 | 105 | 716 | 145 | 95 | 30 | 266 | 125 |
| 4126 | 414 | 41609 | Central | Small Market | Decaf Irish Cream | Beans | Coffee | Wisconsin | Decaf | 80 | 110 | 70 | 190 | 83 | 575 | 112 | 27 | 85 | 208 | 55 |
672 rows × 20 columns
b=d100[['COGS','Sales','Total Expenses','Profit']]
print(b)
COGS Sales Total Expenses Profit 0 89 219 36 94 1 83 190 39 68 2 95 234 38 101 4 54 134 26 54 5 72 180 55 53 ... ... ... ... ... 4122 80 188 46 74 4123 24 60 19 19 4124 65 155 58 33 4125 105 266 125 30 4126 83 208 55 85 [672 rows x 4 columns]
sns.pairplot(b)
plt.show()
11) Depict Product vs Sales based on Market on a Strip Plot where Type is Decaf and Market is not Central<br>
12) Depict State vs Budget Profit based on Product Type on a Strip Plot where Market is not Western and Sales >300<br>
13) Depict Market vs Total Expenses based on Type on a Swarm Plot where Sales is over 200 and Market Size is Small<br>
14) Depict correlation values on a heatmap<br>
15) Depict Budget Sales vs Sales on a Joint Plot<br>
16) Depict Total Expenses on Joint plot where Sales>300 or Profit>300 where joint plot kind is scatter<br>
17) Depict Budget COGS on Joint plot where Market is Eastern and Type is Regular where joint plot kind is hex
<br>
18) Depict Marketing on Joint plot where Market is not Central and Sales is over 500 where joint plot kind is kde<br>
#Q11) Depict Product vs Sales based on Market on a Strip Plot where Type is Decaf and Market is not Central<br>
d94 = df[(df['Market']!='Central')&(df['Type']=='Decaf')]
d94
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 475 | 40909 | East | Small Market | Decaf Espresso | Beans | Espresso | Connecticut | Decaf | 50 | 70 | 40 | 120 | 55 | 312 | 75 | 18 | 30 | 130 | 45 |
| 61 | 860 | 40909 | East | Small Market | Lemon | Leaves | Herbal Tea | Connecticut | Decaf | 60 | 80 | 40 | 140 | 85 | 494 | 105 | 32 | 40 | 190 | 65 |
| 62 | 475 | 40909 | East | Small Market | Mint | Leaves | Herbal Tea | Connecticut | Decaf | 50 | 80 | 50 | 130 | 78 | 965 | 92 | 24 | 47 | 170 | 45 |
| 65 | 754 | 40909 | East | Major Market | Decaf Espresso | Beans | Espresso | Florida | Decaf | 70 | 100 | 60 | 170 | 77 | 432 | 103 | 25 | 50 | 180 | 53 |
| 67 | 754 | 40909 | East | Major Market | Decaf Irish Cream | Beans | Coffee | Florida | Decaf | 80 | 110 | 70 | 190 | 84 | 500 | 116 | 26 | 67 | 200 | 49 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4237 | 541 | 41609 | West | Small Market | Decaf Irish Cream | Beans | Coffee | Oregon | Decaf | 40 | 80 | 50 | 120 | 41 | 435 | 60 | 13 | 37 | 108 | 35 |
| 4241 | 253 | 41609 | West | Small Market | Chamomile | Leaves | Herbal Tea | Washington | Decaf | 100 | 160 | 110 | 260 | 115 | 1166 | 174 | 37 | 156 | 308 | 69 |
| 4242 | 425 | 41609 | West | Small Market | Lemon | Leaves | Herbal Tea | Washington | Decaf | 40 | 70 | 50 | 110 | 49 | 845 | 71 | 13 | 68 | 128 | 25 |
| 4245 | 360 | 41609 | West | Small Market | Decaf Espresso | Beans | Espresso | Washington | Decaf | 70 | 100 | 60 | 170 | 80 | 1079 | 96 | 24 | 76 | 188 | 45 |
| 4247 | 206 | 41609 | West | Small Market | Decaf Irish Cream | Beans | Coffee | Washington | Decaf | 120 | 170 | 50 | 290 | 105 | 716 | 145 | 95 | 30 | 266 | 125 |
1224 rows × 20 columns
sns.stripplot(x="Product",
y='Sales',data=d94,hue='Market')
plt.xticks(rotation=90)
plt.show()
#Q12) Depict State vs Budget Profit based on Product Type on a Strip Plot where Market is not Western and Sales>300
mini = df[(df['Market']!='Western')&(df['Sales']>300)]
mini
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 970 | 40909 | Central | Major Market | Chamomile | Leaves | Herbal Tea | Colorado | Decaf | 140 | 160 | 110 | 300 | 170 | 1091 | 171 | 47 | 99 | 341 | 72 |
| 11 | 217 | 40909 | Central | Major Market | Colombian | Beans | Coffee | Illinois | Regular | 150 | 210 | 130 | 360 | 144 | 862 | 201 | 47 | 111 | 345 | 90 |
| 13 | 309 | 40909 | Central | Major Market | Caffe Mocha | Beans | Espresso | Illinois | Regular | 270 | 370 | 260 | 640 | 234 | 1310 | 312 | 77 | 203 | 546 | 109 |
| 14 | 630 | 40909 | Central | Major Market | Decaf Espresso | Beans | Espresso | Illinois | Decaf | 260 | 270 | 180 | 530 | 228 | 1459 | 228 | 63 | 140 | 456 | 88 |
| 25 | 641 | 40909 | Central | Small Market | Chamomile | Leaves | Herbal Tea | Iowa | Decaf | 200 | 280 | 200 | 480 | 234 | 1310 | 312 | 77 | 202 | 546 | 110 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4220 | 702 | 41609 | West | Small Market | Lemon | Leaves | Herbal Tea | Nevada | Decaf | 210 | 280 | 170 | 490 | 224 | 1191 | 310 | 73 | 288 | 569 | 116 |
| 4221 | 775 | 41609 | West | Small Market | Mint | Leaves | Herbal Tea | Nevada | Decaf | 120 | 170 | 120 | 290 | 127 | 830 | 185 | 40 | 181 | 332 | 63 |
| 4229 | 503 | 41609 | West | Small Market | Green Tea | Leaves | Tea | Oregon | Regular | 90 | 140 | 100 | 230 | 134 | 690 | 186 | 41 | 180 | 341 | 65 |
| 4234 | 971 | 41609 | West | Small Market | Decaf Espresso | Beans | Espresso | Oregon | Decaf | 150 | 140 | 80 | 290 | 153 | 1319 | 153 | 42 | 129 | 326 | 66 |
| 4241 | 253 | 41609 | West | Small Market | Chamomile | Leaves | Herbal Tea | Washington | Decaf | 100 | 160 | 110 | 260 | 115 | 1166 | 174 | 37 | 156 | 308 | 69 |
674 rows × 20 columns
sns.stripplot(x="State",
y='Budget Profit',data=d94,hue='Product Type')
plt.xticks(rotation=90)
plt.show()
#Q13) Depict Market vs Total Expenses based on Type on a Swarm Plot where Sales is over 200 and Market Size is Small
ddd = df[(df['Market Size']=='Small Market') & (df['Sales']>200)]
ddd
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 25 | 641 | 40909 | Central | Small Market | Chamomile | Leaves | Herbal Tea | Iowa | Decaf | 200 | 280 | 200 | 480 | 234 | 1310 | 312 | 77 | 202 | 546 | 110 |
| 26 | 563 | 40909 | Central | Small Market | Lemon | Leaves | Herbal Tea | Iowa | Decaf | 80 | 120 | 90 | 200 | 95 | 608 | 139 | 30 | 86 | 234 | 53 |
| 27 | 712 | 40909 | Central | Small Market | Darjeeling | Leaves | Tea | Iowa | Regular | 190 | 210 | 140 | 400 | 228 | 1459 | 228 | 63 | 141 | 456 | 87 |
| 28 | 319 | 40909 | Central | Small Market | Earl Grey | Leaves | Tea | Iowa | Regular | 210 | 270 | 160 | 480 | 245 | 1419 | 301 | 93 | 175 | 546 | 126 |
| 60 | 475 | 40909 | East | Small Market | Colombian | Beans | Coffee | Connecticut | Regular | 110 | 180 | 130 | 290 | 124 | 961 | 186 | 40 | 115 | 310 | 71 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4228 | 971 | 41609 | West | Small Market | Earl Grey | Leaves | Tea | Oregon | Regular | 60 | 100 | 60 | 160 | 88 | 817 | 133 | 29 | 108 | 236 | 60 |
| 4229 | 503 | 41609 | West | Small Market | Green Tea | Leaves | Tea | Oregon | Regular | 90 | 140 | 100 | 230 | 134 | 690 | 186 | 41 | 180 | 341 | 65 |
| 4234 | 971 | 41609 | West | Small Market | Decaf Espresso | Beans | Espresso | Oregon | Decaf | 150 | 140 | 80 | 290 | 153 | 1319 | 153 | 42 | 129 | 326 | 66 |
| 4241 | 253 | 41609 | West | Small Market | Chamomile | Leaves | Herbal Tea | Washington | Decaf | 100 | 160 | 110 | 260 | 115 | 1166 | 174 | 37 | 156 | 308 | 69 |
| 4247 | 206 | 41609 | West | Small Market | Decaf Irish Cream | Beans | Coffee | Washington | Decaf | 120 | 170 | 50 | 290 | 105 | 716 | 145 | 95 | 30 | 266 | 125 |
506 rows × 20 columns
sns.swarmplot(x="Market",
y='Total Expenses',data=ddd,hue='Type')
plt.xticks(rotation=90)
plt.show()
C:\ProgramData\Anaconda3\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 7.1% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning) C:\ProgramData\Anaconda3\lib\site-packages\seaborn\categorical.py:1296: UserWarning: 19.9% of the points cannot be placed; you may want to decrease the size of the markers or use stripplot. warnings.warn(msg, UserWarning)
#Q15) Depict Budget Sales vs Sales on a Joint Plot
sns.jointplot(x=df['Budget Sales'],y=df['Sales'],kind='reg',color='yellow')
plt.show()
#Q16) Depict Total Expenses on Joint plot where Sales>300 or Profit>300 where joint plot kind is scatter<
dss = df[(df['Sales']>300) | (df['Profit']>300)]
dss
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 6 | 970 | 40909 | Central | Major Market | Chamomile | Leaves | Herbal Tea | Colorado | Decaf | 140 | 160 | 110 | 300 | 170 | 1091 | 171 | 47 | 99 | 341 | 72 |
| 11 | 217 | 40909 | Central | Major Market | Colombian | Beans | Coffee | Illinois | Regular | 150 | 210 | 130 | 360 | 144 | 862 | 201 | 47 | 111 | 345 | 90 |
| 13 | 309 | 40909 | Central | Major Market | Caffe Mocha | Beans | Espresso | Illinois | Regular | 270 | 370 | 260 | 640 | 234 | 1310 | 312 | 77 | 203 | 546 | 109 |
| 14 | 630 | 40909 | Central | Major Market | Decaf Espresso | Beans | Espresso | Illinois | Decaf | 260 | 270 | 180 | 530 | 228 | 1459 | 228 | 63 | 140 | 456 | 88 |
| 25 | 641 | 40909 | Central | Small Market | Chamomile | Leaves | Herbal Tea | Iowa | Decaf | 200 | 280 | 200 | 480 | 234 | 1310 | 312 | 77 | 202 | 546 | 110 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4220 | 702 | 41609 | West | Small Market | Lemon | Leaves | Herbal Tea | Nevada | Decaf | 210 | 280 | 170 | 490 | 224 | 1191 | 310 | 73 | 288 | 569 | 116 |
| 4221 | 775 | 41609 | West | Small Market | Mint | Leaves | Herbal Tea | Nevada | Decaf | 120 | 170 | 120 | 290 | 127 | 830 | 185 | 40 | 181 | 332 | 63 |
| 4229 | 503 | 41609 | West | Small Market | Green Tea | Leaves | Tea | Oregon | Regular | 90 | 140 | 100 | 230 | 134 | 690 | 186 | 41 | 180 | 341 | 65 |
| 4234 | 971 | 41609 | West | Small Market | Decaf Espresso | Beans | Espresso | Oregon | Decaf | 150 | 140 | 80 | 290 | 153 | 1319 | 153 | 42 | 129 | 326 | 66 |
| 4241 | 253 | 41609 | West | Small Market | Chamomile | Leaves | Herbal Tea | Washington | Decaf | 100 | 160 | 110 | 260 | 115 | 1166 | 174 | 37 | 156 | 308 | 69 |
674 rows × 20 columns
sns.jointplot(x=dss['Total Expenses'],y=dss['Sales'],color='green',kind='scatter')
plt.show()
#Q17) Depict Budget COGS on Joint plot where Market is Eastern and Type is Regular where joint plot kind is hex
diy = df[(df['Market']=='East')&(df['Type']=='Regular')]
diy
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 56 | 475 | 40909 | East | Small Market | Darjeeling | Leaves | Tea | Connecticut | Regular | 50 | 80 | 60 | 130 | 50 | 821 | 73 | 14 | 47 | 123 | 26 |
| 57 | 475 | 40909 | East | Small Market | Green Tea | Leaves | Tea | Connecticut | Regular | 40 | 60 | 40 | 100 | 38 | 777 | 56 | 10 | 35 | 94 | 21 |
| 59 | 860 | 40909 | East | Small Market | Caffe Mocha | Beans | Espresso | Connecticut | Regular | 70 | 100 | 20 | 170 | 75 | 522 | 105 | 68 | 7 | 180 | 98 |
| 60 | 475 | 40909 | East | Small Market | Colombian | Beans | Coffee | Connecticut | Regular | 110 | 180 | 130 | 290 | 124 | 961 | 186 | 40 | 115 | 310 | 71 |
| 63 | 954 | 40909 | East | Major Market | Darjeeling | Leaves | Tea | Florida | Regular | 30 | 50 | 40 | 80 | 32 | 821 | 48 | 8 | 28 | 80 | 20 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4159 | 518 | 41609 | East | Major Market | Earl Grey | Leaves | Tea | New York | Regular | 120 | 190 | 150 | 310 | 123 | 959 | 179 | 34 | 197 | 322 | 46 |
| 4160 | 585 | 41609 | East | Major Market | Green Tea | Leaves | Tea | New York | Regular | 50 | 80 | 60 | 130 | 54 | 601 | 79 | 15 | 77 | 142 | 27 |
| 4161 | 845 | 41609 | East | Major Market | Caffe Mocha | Beans | Espresso | New York | Regular | 130 | -70 | -210 | 60 | 135 | 3641 | -69 | 122 | -332 | 70 | 155 |
| 4162 | 518 | 41609 | East | Major Market | Regular Espresso | Beans | Espresso | New York | Regular | 240 | 400 | 320 | 640 | 250 | 723 | 407 | 70 | 463 | 700 | 95 |
| 4163 | 315 | 41609 | East | Major Market | Colombian | Beans | Coffee | New York | Regular | 250 | 370 | 250 | 620 | 279 | 2642 | 420 | 97 | 402 | 745 | 149 |
600 rows × 20 columns
#sns.jointplot(x=diy['Type'],y=diy['Budget COGS'],kind='hex',color='red')
#plt.show()
#Q18) Depict Marketing on Joint plot where Market is not Central and Sales is over 500 where joint plot kind is kde<
dw = df[(df['Market']!='Central') & (df['Sales']>500)]
dw
| Area Code | Date | Market | Market Size | Product | Product Line | Product Type | State | Type | Budget COGS | Budget Margin | Budget Profit | Budget Sales | COGS | Inventory | Margin | Marketing | Profit | Sales | Total Expenses | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 90 | 716 | 40909 | East | Major Market | Colombian | Beans | Coffee | New York | Regular | 260 | 380 | 260 | 640 | 271 | 2101 | 407 | 94 | 262 | 678 | 145 |
| 135 | 213 | 40909 | West | Major Market | Decaf Espresso | Beans | Espresso | California | Decaf | 220 | 300 | 200 | 520 | 234 | 1310 | 312 | 77 | 203 | 546 | 109 |
| 143 | 559 | 40909 | West | Major Market | Colombian | Beans | Coffee | California | Regular | 340 | 500 | 370 | 840 | 271 | 2101 | 407 | 94 | 262 | 678 | 145 |
| 152 | 775 | 40909 | West | Small Market | Darjeeling | Leaves | Tea | Nevada | Regular | 150 | 220 | 150 | 370 | 234 | 1310 | 312 | 77 | 203 | 546 | 109 |
| 265 | 585 | 40940 | East | Major Market | Regular Espresso | Beans | Espresso | New York | Regular | 210 | 350 | 280 | 560 | 228 | 1402 | 352 | 63 | 264 | 580 | 88 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 4212 | 209 | 41609 | West | Major Market | Decaf Espresso | Beans | Espresso | California | Decaf | 240 | 310 | 210 | 550 | 247 | 1744 | 329 | 81 | 321 | 614 | 113 |
| 4214 | 530 | 41609 | West | Major Market | Colombian | Beans | Coffee | California | Regular | 330 | 500 | 350 | 830 | 279 | 2642 | 420 | 97 | 402 | 745 | 149 |
| 4216 | 775 | 41609 | West | Small Market | Darjeeling | Leaves | Tea | Nevada | Regular | 180 | 240 | 150 | 420 | 247 | 1744 | 329 | 81 | 319 | 614 | 114 |
| 4217 | 775 | 41609 | West | Small Market | Earl Grey | Leaves | Tea | Nevada | Regular | 180 | 180 | 100 | 360 | 250 | 1820 | 251 | 70 | 233 | 534 | 94 |
| 4220 | 702 | 41609 | West | Small Market | Lemon | Leaves | Herbal Tea | Nevada | Decaf | 210 | 280 | 170 | 490 | 224 | 1191 | 310 | 73 | 288 | 569 | 116 |
225 rows × 20 columns
sns.jointplot(x=dw['Sales'],y=dw['Marketing'],kind='kde',color='brown')
plt.show()